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dc.contributor.authorVivian, Tsz-Yan Tangen_US
dc.contributor.authorLiew, Alan Wee-Chungen_US
dc.contributor.authorYan, Hongen_US
dc.date.accessioned2017-05-03T15:20:03Z
dc.date.available2017-05-03T15:20:03Z
dc.date.issued2010en_US
dc.date.modified2011-03-23T05:46:10Z
dc.identifier.issn19387989en_US
dc.identifier.doi10.4310/SII.2010.v3.n3.a13en_AU
dc.identifier.urihttp://hdl.handle.net/10072/37622
dc.description.abstractWith microarray technology, gene expression profiles are produced at a rapid rate. It remains a challenge for biologists to robustly identify periodic gene expression profiles when the time series have short data length and contain a high level of noise. An effective method is proposed in this paper to analyze the periodicity of gene expression time series using singular value decomposition (SVD), singular spectrum analysis (SSA) and autoregressive (AR) model-based spectral estimation. Using these procedures, noise can be filtered out and over 85% of periodic gene expression can be identified in the mouse segmentation clock data set.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherInternational Pressen_US
dc.publisher.placeUnited Statesen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom413en_US
dc.relation.ispartofpageto418en_US
dc.relation.ispartofissue3en_US
dc.relation.ispartofjournalStatistics and its interfaceen_US
dc.relation.ispartofvolume3en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchPattern Recognition and Data Miningen_US
dc.subject.fieldofresearchcode080109en_US
dc.titlePeriodicity analysis of DNA microarray gene expression time series profiles in mouse segmentation clock dataen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.date.issued2010
gro.hasfulltextNo Full Text


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